Difference between revisions of "Posterior Regularization for Expectation Maximization"

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The goal of this method is to define a way to constrains over posteriors, so that prior information can be set over these posteriors by defining a contraint set <math>Q(x)</math>. This method addresses this, by
 
The goal of this method is to define a way to constrains over posteriors, so that prior information can be set over these posteriors by defining a contraint set <math>Q(x)</math>. This method addresses this, by
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== Applications ==
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Posterior regularization has been used to improve [[Word Alignments]] in [www.seas.upenn.edu/~taskar/pubs/cl10.pdfSimilar Graca et al, 2010], by defining bijectivity and symmetry constraints over alignment posteriors.

Revision as of 23:31, 29 September 2011

Summary

The Expectation Maximization algorithm is a method for finding the maximum likelihood estimates for the parameters in a statistical model. During the E-step of this algorithm, posterior probabilities are calculated for the latent data by fixing the parameters.

In many fields, prior knowledge about the posterior probabilities are known and can be applied to the model to improve the statistical model, yet the method to include such knowledge is not always clear.

Posterior Regularization is a method used to impose contraints on posteriors in the Expectation Maximization algorithm, allowing a finer-level control over these posteriors, which allows prior knowledge to be defined.

Method Description

For a given set of observed data, a set of latent data and a set of parameters , the Expectation Maximization algorithm can be viewed as the alternation between two maximization steps of the function , by marginalizing different free variables.

The E-step is defined as:

where is the Kullback-Leibler divergence given by , q(z|x) is an arbitrary probability distribution over the latent variable z and is the posterior probability for z, for the fixed parameters .

The new is then used in the M-step, which is defined as:

The goal of this method is to define a way to constrains over posteriors, so that prior information can be set over these posteriors by defining a contraint set . This method addresses this, by

Applications

Posterior regularization has been used to improve Word Alignments in [www.seas.upenn.edu/~taskar/pubs/cl10.pdfSimilar Graca et al, 2010], by defining bijectivity and symmetry constraints over alignment posteriors.